A comparison of SVM-based cascade multitemporal classifiers

In this work we compare empirically five cascade classification schemes based on Support Vector Machines. Data fusion as well as decision fusion variants are considered. Data fusion is implemented by simply stacking feature vectors, whereas decision fusion is performed by a multitemporal SVM classifier, which classifies input patterns consisting of probability vectors produced by monotemporal SVMs. The exploitation of prior knowledge in terms of possible class transitions is a further aspect investigated in the present paper. The analysis is conducted upon a pair of IKONOS images from Rio de Janeiro, Brazil. The study reveals that a considerable accuracy improvement may be brought by the multitemporal approaches regarding their monotemporal counterparts. In particular, for the decision fusion schemes, the improvement is highly dependent on the relative accuracy of the monotemporal classifiers, whose individual decisions are combined to produce a consensual decision.